SHOGUN  6.1.3
CMMD Class Referenceabstract

## Detailed Description

Abstract base class that provides an interface for performing kernel two-sample test using Maximum Mean Discrepancy (MMD) as the test statistic. The MMD is the distance of two probability distributions $$p$$ and $$q$$ in a RKHS (see [1] for formal description).

$\text{MMD}[\mathcal{F},p,q]^2=||\mu_p - \mu_q||^2_\mathcal{F}= \textbf{E}_{x,x'}\left[ k(x,x')\right] -2\textbf{E}_{x,y}\left[ k(x,y)\right] +\textbf{E}_{y,y'}\left[ k(y,y')\right]$

where $$x,x'\sim p$$ and $$y,y'\sim q$$.

Given two sets of samples $$\{x_i\}_{i=1}^{n_x}\sim p$$ and $$\{y_i\}_{i=1}^{n_y}\sim q$$, $$n_x+n_y=n$$, the unbiased estimate of the above statistic is computed as

$\hat{\eta}_{k,U}=\frac{1}{n_x(n_x-1)}\sum_{i=1}^{n_x}\sum_{j\neq i} k(x_i,x_j)+\frac{1}{n_y(n_y-1)}\sum_{i=1}^{n_y}\sum_{j\neq i}k(y_i,y_j) -\frac{2}{n_xn_y}\sum_{i=1}^{n_x}\sum_{j=1}^{n_y}k(x_i,y_j)$

A biased version is

$\hat{\eta}_{k,V}=\frac{1}{n_x^2}\sum_{i=1}^{n_x}\sum_{j=1}^{n_x} k(x_i,x_j)+\frac{1}{n_y^2}\sum_{i=1}^{n_y}\sum_{j=1}^{n_y}k(y_i,y_j) -\frac{2}{n_xn_y}\sum_{i=1}^{n_x}\sum_{j=1}^{n_y}k(x_i,y_j)$

When $$n_x=n_y=\frac{n}{2}$$, an incomplete version can also be computed as the following

$\hat{\eta}_{k,U^-}=\frac{1}{\frac{n}{2}(\frac{n}{2}-1)}\sum_{i\neq j} h(z_i,z_j)$

where for each pair $$z=(x,y)$$, $$h(z,z')=k(x,x')+k(y,y')-k(x,y')- k(x',y)$$.

The type (biased/unbiased/incomplete) can be selected via set_statistic_type() via the enum values from EStatisticType, ST_BIASED, ST_UNBIASED and ST_INCOMPLETE, respectively. The estimate returned by compute_statistic() is $$\frac{n_xn_y}{n_x+n_y}\hat{\eta}_k$$.

This class provides an interface for adding multiple kernels and then selecting the best kernel based on specified strategies. To know more in details about various learning algorithms for optimal kernel selection, please refer to [2].

Along with the statistic comes a method to compute a p-value based on different methods. Permutation test is possible. If unsure which one to use, sampling with 250 permutation iterations usually always is correct.

To choose, use set_null_approximation_method() and choose from.

NAM_MMD2_SPECTRUM: For a fast, consistent test based on the spectrum of the kernel matrix, as described in [2]. Only supported if Eigen3 is installed. Only applicable for CQuadraticTimeMMD.

NAM_MMD2_GAMMA: for a very fast, but not consistent test based on moment matching of a Gamma distribution, as described in [2]. Only applicable for CQuadraticTimeMMD.

NAM_PERMUTATION: For permuting available samples to sample null-distribution

[1]: Gretton, A., Borgwardt, K. M., Rasch, M. J., Schoelkopf, B., & Smola, A. (2012). A Kernel Two-Sample Test. Journal of Machine Learning Research, 13, 671-721.

[2] Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu: Optimal kernel choice for large-scale two-sample tests. NIPS 2012: 1214-1222.

Definition at line 120 of file MMD.h.

Inheritance diagram for CMMD:
[legend]

struct  Self

## Public Types

typedef rxcpp::subjects::subject< ObservedValueSGSubject

typedef rxcpp::observable< ObservedValue, rxcpp::dynamic_observable< ObservedValue > > SGObservable

typedef rxcpp::subscriber< ObservedValue, rxcpp::observer< ObservedValue, void, void, void, void > > SGSubscriber

## Public Member Functions

CMMD ()

CMMD (CFeatures *samples_from_p, CFeatures *samples_from_q)

virtual ~CMMD ()

void set_kernel_selection_strategy (EKernelSelectionMethod method, bool weighted=false)

void set_kernel_selection_strategy (EKernelSelectionMethod method, index_t num_runs, index_t num_folds, float64_t alpha)

virtual void select_kernel ()

CKernelSelectionStrategy const * get_kernel_selection_strategy () const

virtual float64_t compute_statistic ()=0

virtual SGVector< float64_tsample_null ()=0

void cleanup ()

void set_num_null_samples (index_t null_samples)

index_t get_num_null_samples () const

void set_statistic_type (EStatisticType stype)

EStatisticType get_statistic_type () const

void set_null_approximation_method (ENullApproximationMethod nmethod)

ENullApproximationMethod get_null_approximation_method () const

virtual const char * get_name () const

virtual void set_kernel (CKernel *kernel)

CKernelget_kernel () const

virtual void set_p (CFeatures *samples_from_p)

CFeaturesget_p () const

virtual void set_q (CFeatures *samples_from_q)

CFeaturesget_q () const

void set_num_samples_p (index_t num_samples_from_p)

const index_t get_num_samples_p () const

void set_num_samples_q (index_t num_samples_from_q)

const index_t get_num_samples_q () const

CCustomDistancecompute_distance (CDistance *distance)

CCustomDistancecompute_joint_distance (CDistance *distance)

void set_train_test_mode (bool on)

void set_train_test_ratio (float64_t ratio)

virtual float64_t compute_p_value (float64_t statistic)

virtual float64_t compute_threshold (float64_t alpha)

bool perform_test (float64_t alpha)

int32_t ref ()

int32_t ref_count ()

int32_t unref ()

virtual CSGObjectshallow_copy () const

virtual CSGObjectdeep_copy () const

virtual bool is_generic (EPrimitiveType *generic) const

template<class T >
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

template<>
void set_generic ()

void unset_generic ()

virtual void print_serializable (const char *prefix="")

virtual bool save_serializable (CSerializableFile *file, const char *prefix="")

virtual bool load_serializable (CSerializableFile *file, const char *prefix="")

void set_global_io (SGIO *io)

SGIOget_global_io ()

void set_global_parallel (Parallel *parallel)

Parallelget_global_parallel ()

void set_global_version (Version *version)

Versionget_global_version ()

SGStringList< char > get_modelsel_names ()

void print_modsel_params ()

char * get_modsel_param_descr (const char *param_name)

index_t get_modsel_param_index (const char *param_name)

void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject *> *dict)

bool has (const std::string &name) const

template<typename T >
bool has (const Tag< T > &tag) const

template<typename T , typename U = void>
bool has (const std::string &name) const

template<typename T >
void set (const Tag< T > &_tag, const T &value)

template<typename T , typename U = void>
void set (const std::string &name, const T &value)

template<typename T >
get (const Tag< T > &_tag) const

template<typename T , typename U = void>
get (const std::string &name) const

SGObservableget_parameters_observable ()

void subscribe_to_parameters (ParameterObserverInterface *obs)

void list_observable_parameters ()

virtual void update_parameter_hash ()

virtual bool parameter_hash_changed ()

virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)

virtual CSGObjectclone ()

## Public Attributes

SGIOio

Parallelparallel

Versionversion

Parameterm_parameters

Parameterm_model_selection_parameters

uint32_t m_hash

## Protected Member Functions

virtual float64_t normalize_statistic (float64_t statistic) const =0

internal::KernelManager & get_kernel_mgr ()

const internal::KernelManager & get_kernel_mgr () const

internal::DataManagerget_data_mgr ()

const internal::DataManagerget_data_mgr () const

virtual void load_serializable_pre () throw (ShogunException)

virtual void load_serializable_post () throw (ShogunException)

virtual void save_serializable_pre () throw (ShogunException)

virtual void save_serializable_post () throw (ShogunException)

template<typename T >
void register_param (Tag< T > &_tag, const T &value)

template<typename T >
void register_param (const std::string &name, const T &value)

bool clone_parameters (CSGObject *other)

void observe (const ObservedValue value)

void register_observable_param (const std::string &name, const SG_OBS_VALUE_TYPE type, const std::string &description)

## ◆ SGObservable

 inherited

Definition at line 130 of file SGObject.h.

## ◆ SGSubject

 inherited

Definition at line 127 of file SGObject.h.

## ◆ SGSubscriber

 typedef rxcpp::subscriber< ObservedValue, rxcpp::observer > SGSubscriber
inherited

Definition at line 133 of file SGObject.h.

## ◆ CMMD() [1/2]

 CMMD ( )

Default constructor

Definition at line 66 of file MMD.cpp.

## ◆ CMMD() [2/2]

 CMMD ( CFeatures * samples_from_p, CFeatures * samples_from_q )

Convenience constructor that initializes the samples from two distributions.

Parameters
 samples_from_p Samples from $$p$$ samples_from_q Samples from $$q$$

Definition at line 71 of file MMD.cpp.

## ◆ ~CMMD()

 ~CMMD ( )
virtual

Destructor

Definition at line 84 of file MMD.cpp.

## Member Function Documentation

 void add_kernel ( CKernel * kernel )

Method that adds a kernel instance to be used for kernel selection. Please note that the kernels added by this method are NOT set as the main test kernel unless select_kernel() method is executed.

Parameters
 kernel One of the kernel instances with which learning algorithm will work.

Definition at line 109 of file MMD.cpp.

 void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject *> * dict )
inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters
 dict dictionary of parameters to be built.

Definition at line 635 of file SGObject.cpp.

## ◆ cleanup()

 void cleanup ( )

Method that releases the pre-computed kernel that is used in the computation.

Definition at line 124 of file MMD.cpp.

## ◆ clone()

 CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 734 of file SGObject.cpp.

## ◆ clone_parameters()

 bool clone_parameters ( CSGObject * other )
protectedinherited

Definition at line 759 of file SGObject.cpp.

## ◆ compute_distance()

 CCustomDistance * compute_distance ( CDistance * distance )
inherited

Method that pre-computes the pair-wise distance between the samples using the provided distance instance.

Parameters
 distance The distance instance used for pre-computing the pair-wise distance.
Returns
A newly created CCustomDistance instance representing the pre-computed pair-wise distance between the samples.

Definition at line 99 of file TwoDistributionTest.cpp.

## ◆ compute_joint_distance()

 CCustomDistance * compute_joint_distance ( CDistance * distance )
inherited

Method that pre-computes the pair-wise distance between the joint samples using the provided distance instance. A temporary object appending the samples from both the distributions is created in order to perform the task.

Parameters
 distance The distance instance used for pre-computing the pair-wise distance.
Returns
A newly created CCustomDistance instance representing the pre-computed pair-wise distance between the joint samples.

Definition at line 128 of file TwoDistributionTest.cpp.

## ◆ compute_p_value()

 float64_t compute_p_value ( float64_t statistic )
virtualinherited

Method that computes a p-value based on current method for approximating the null-distribution. The p-value is the 1-p quantile of the null- distribution where the given statistic lies in.

This method depends on the implementation of sample_null method which should be implemented by the sub-classes.

Parameters
 statistic statistic value to compute the p-value for
Returns
p-value parameter statistic is the (1-p) percentile of the null distribution

Reimplemented in CQuadraticTimeMMD, CBTestMMD, and CLinearTimeMMD.

Definition at line 77 of file HypothesisTest.cpp.

## ◆ compute_statistic()

 virtual float64_t compute_statistic ( )
pure virtual

Interface for computing the test-statistic for the hypothesis test.

Returns
test statistic for the given data/parameters/methods

Implements CTwoSampleTest.

## ◆ compute_threshold()

 float64_t compute_threshold ( float64_t alpha )
virtualinherited

Method that computes a threshold based on current method for approximating the null-distribution. The threshold is the value that a statistic has to have in ordner to reject the null-hypothesis.

This method depends on the implementation of sample_null method which should be implemented by the sub-classes.

Parameters
 alpha test level to reject null-hypothesis
Returns
Threshold for statistics to reject null-hypothesis

Reimplemented in CQuadraticTimeMMD, CBTestMMD, and CLinearTimeMMD.

Definition at line 85 of file HypothesisTest.cpp.

## ◆ deep_copy()

 CSGObject * deep_copy ( ) const
virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 232 of file SGObject.cpp.

## ◆ equals()

 bool equals ( CSGObject * other, float64_t accuracy = 0.0, bool tolerant = false )
virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters
 other object to compare with accuracy accuracy to use for comparison (optional) tolerant allows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 656 of file SGObject.cpp.

## ◆ get() [1/2]

 T get ( const Tag< T > & _tag ) const
inherited

Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
 _tag name and type information of parameter
Returns
value of the parameter identified by the input tag

Definition at line 381 of file SGObject.h.

## ◆ get() [2/2]

 T get ( const std::string & name ) const
inherited

Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
 name name of the parameter
Returns
value of the parameter corresponding to the input name and type

Definition at line 404 of file SGObject.h.

## ◆ get_data_mgr() [1/2]

 DataManager & get_data_mgr ( )
protectedinherited

Definition at line 104 of file HypothesisTest.cpp.

## ◆ get_data_mgr() [2/2]

 const DataManager & get_data_mgr ( ) const
protectedinherited

Definition at line 109 of file HypothesisTest.cpp.

## ◆ get_global_io()

 SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 269 of file SGObject.cpp.

## ◆ get_global_parallel()

 Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 311 of file SGObject.cpp.

## ◆ get_global_version()

 Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 324 of file SGObject.cpp.

## ◆ get_kernel()

 CKernel * get_kernel ( ) const
inherited
Returns
The kernel instance that is presently being used for performing the test

Definition at line 73 of file TwoSampleTest.cpp.

## ◆ get_kernel_mgr() [1/2]

 KernelManager & get_kernel_mgr ( )
protectedinherited

Definition at line 83 of file TwoSampleTest.cpp.

## ◆ get_kernel_mgr() [2/2]

 const KernelManager & get_kernel_mgr ( ) const
protectedinherited

Definition at line 88 of file TwoSampleTest.cpp.

## ◆ get_kernel_selection_strategy()

 CKernelSelectionStrategy const * get_kernel_selection_strategy ( ) const

Method that returns the kernel selection strategy wrapper object that will be/ was used in the last kernel learning algorithm. Use this method when results of intermediate steps taken by the kernel selection algorithms are of interest.

Returns
The internal instance of CKernelSelectionStrategy that holds intermediate measures computed at the time of the last kernel selection algorithm invocation.

Definition at line 104 of file MMD.cpp.

## ◆ get_modelsel_names()

 SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 536 of file SGObject.cpp.

## ◆ get_modsel_param_descr()

 char * get_modsel_param_descr ( const char * param_name )
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters
 param_name name of the parameter
Returns
description of the parameter

Definition at line 560 of file SGObject.cpp.

## ◆ get_modsel_param_index()

 index_t get_modsel_param_index ( const char * param_name )
inherited

Returns index of model selection parameter with provided index

Parameters
 param_name name of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 573 of file SGObject.cpp.

## ◆ get_name()

 const char * get_name ( ) const
virtual
Returns
The name of this class

Reimplemented from CTwoSampleTest.

Reimplemented in CQuadraticTimeMMD, CStreamingMMD, CBTestMMD, and CLinearTimeMMD.

Definition at line 159 of file MMD.cpp.

## ◆ get_null_approximation_method()

 ENullApproximationMethod get_null_approximation_method ( ) const
Returns
The null-approximation method

Definition at line 154 of file MMD.cpp.

## ◆ get_num_null_samples()

 index_t get_num_null_samples ( ) const
Returns
Number of null-samples

Definition at line 134 of file MMD.cpp.

## ◆ get_num_samples_p()

 const index_t get_num_samples_p ( ) const
inherited
Returns
The number of samples from $$\mathbf{P}$$.

Definition at line 81 of file TwoDistributionTest.cpp.

## ◆ get_num_samples_q()

 const index_t get_num_samples_q ( ) const
inherited
Returns
The number of samples from $$\mathbf{Q}$$.

Definition at line 93 of file TwoDistributionTest.cpp.

## ◆ get_p()

 CFeatures * get_p ( ) const
inherited
Returns
The samples from $$\mathbf{P}$$.

Definition at line 56 of file TwoDistributionTest.cpp.

## ◆ get_parameters_observable()

 SGObservable* get_parameters_observable ( )
inherited

Get parameters observable

Returns
RxCpp observable

Definition at line 415 of file SGObject.h.

## ◆ get_q()

 CFeatures * get_q ( ) const
inherited
Returns
The samples from $$\mathbf{Q}$$.

Definition at line 69 of file TwoDistributionTest.cpp.

## ◆ get_statistic_type()

 EStatisticType get_statistic_type ( ) const
Returns
The type of the estimator for MMD^2

Definition at line 144 of file MMD.cpp.

## ◆ has() [1/3]

 bool has ( const std::string & name ) const
inherited

Checks if object has a class parameter identified by a name.

Parameters
 name name of the parameter
Returns
true if the parameter exists with the input name

Definition at line 304 of file SGObject.h.

## ◆ has() [2/3]

 bool has ( const Tag< T > & tag ) const
inherited

Checks if object has a class parameter identified by a Tag.

Parameters
 tag tag of the parameter containing name and type information
Returns
true if the parameter exists with the input tag

Definition at line 315 of file SGObject.h.

## ◆ has() [3/3]

 bool has ( const std::string & name ) const
inherited

Checks if a type exists for a class parameter identified by a name.

Parameters
 name name of the parameter
Returns
true if the parameter exists with the input name and type

Definition at line 326 of file SGObject.h.

## ◆ is_generic()

 bool is_generic ( EPrimitiveType * generic ) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
 generic set to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 330 of file SGObject.cpp.

## ◆ list_observable_parameters()

 void list_observable_parameters ( )
inherited

Print to stdout a list of observable parameters

Definition at line 878 of file SGObject.cpp.

 bool load_serializable ( CSerializableFile * file, const char * prefix = "" )
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
 file where to load from prefix prefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 403 of file SGObject.cpp.

 void load_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 460 of file SGObject.cpp.

 void load_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 455 of file SGObject.cpp.

## ◆ normalize_statistic()

 virtual float64_t normalize_statistic ( float64_t statistic ) const
protectedpure virtual

## ◆ observe()

 void observe ( const ObservedValue value )
protectedinherited

Observe a parameter value and emit them to observer.

Parameters
 value Observed parameter's value

Definition at line 828 of file SGObject.cpp.

## ◆ parameter_hash_changed()

 bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 296 of file SGObject.cpp.

## ◆ perform_test()

 bool perform_test ( float64_t alpha )
inherited

Method that performs the complete hypothesis test on current data and returns a binary answer: wheter null hypothesis is rejected or not.

This is just a wrapper for the above compute_p_value() method that returns a p-value. If this p-value lies below the test level alpha, the null hypothesis is rejected.

Should not be overwritten in subclasses. (Therefore not virtual)

Parameters
 alpha test level alpha.
Returns
true if null hypothesis is rejected and false otherwise

Definition at line 92 of file HypothesisTest.cpp.

## ◆ print_modsel_params()

 void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 512 of file SGObject.cpp.

## ◆ print_serializable()

 void print_serializable ( const char * prefix = "" )
virtualinherited

prints registered parameters out

Parameters
 prefix prefix for members

Definition at line 342 of file SGObject.cpp.

## ◆ ref()

 int32_t ref ( )
inherited

increase reference counter

Returns
reference count

Definition at line 186 of file SGObject.cpp.

## ◆ ref_count()

 int32_t ref_count ( )
inherited

display reference counter

Returns
reference count

Definition at line 193 of file SGObject.cpp.

## ◆ register_observable_param()

 void register_observable_param ( const std::string & name, const SG_OBS_VALUE_TYPE type, const std::string & description )
protectedinherited

Register which params this object can emit.

Parameters
 name the param name type the param type description a user oriented description

Definition at line 871 of file SGObject.cpp.

## ◆ register_param() [1/2]

 void register_param ( Tag< T > & _tag, const T & value )
protectedinherited

Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
 _tag name and type information of parameter value value of the parameter

Definition at line 472 of file SGObject.h.

## ◆ register_param() [2/2]

 void register_param ( const std::string & name, const T & value )
protectedinherited

Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.

Parameters
 name name of the parameter value value of the parameter along with type information

Definition at line 485 of file SGObject.h.

## ◆ sample_null()

 virtual SGVector sample_null ( )
pure virtual

Interface for computing the samples under the null-hypothesis.

Returns
vector of all statistics

Implements CTwoSampleTest.

## ◆ save_serializable()

 bool save_serializable ( CSerializableFile * file, const char * prefix = "" )
virtualinherited

Save this object to file.

Parameters
 file where to save the object; will be closed during returning if PREFIX is an empty string. prefix prefix for members
Returns
TRUE if done, otherwise FALSE

Definition at line 348 of file SGObject.cpp.

## ◆ save_serializable_post()

 void save_serializable_post ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 470 of file SGObject.cpp.

## ◆ save_serializable_pre()

 void save_serializable_pre ( ) throw ( ShogunException )
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions
 ShogunException will be thrown if an error occurs.

Definition at line 465 of file SGObject.cpp.

## ◆ select_kernel()

 void select_kernel ( )
virtual

Method that selects/learns the kernel based on the defined kernel selection strategy. If no explicit kernel selection strategy was set using set_kernel_selection_strategy() method, then a default strategy is used. Please see EKernelSelectionMethod for the default strategy.

This method is NOT thread safe. It replaces the internel kernel set by set_kernel() method, if there was any. Please DO NOT use this method from multiple threads.

The learned/selected kernel can be obtained from a subsequent get_kernel() call.

This method expects train-test mode to be turned on at the time of invocation. Please see the class documentation of CHypothesisTest.

Definition at line 114 of file MMD.cpp.

## ◆ set() [1/2]

 void set ( const Tag< T > & _tag, const T & value )
inherited

Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.

Parameters
 _tag name and type information of parameter value value of the parameter

Definition at line 342 of file SGObject.h.

## ◆ set() [2/2]

 void set ( const std::string & name, const T & value )
inherited

Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.

Parameters
 name name of the parameter value value of the parameter along with type information

Definition at line 368 of file SGObject.h.

## ◆ set_generic() [1/16]

 void set_generic ( )
inherited

Definition at line 73 of file SGObject.cpp.

## ◆ set_generic() [2/16]

 void set_generic ( )
inherited

Definition at line 78 of file SGObject.cpp.

## ◆ set_generic() [3/16]

 void set_generic ( )
inherited

Definition at line 83 of file SGObject.cpp.

## ◆ set_generic() [4/16]

 void set_generic ( )
inherited

Definition at line 88 of file SGObject.cpp.

## ◆ set_generic() [5/16]

 void set_generic ( )
inherited

Definition at line 93 of file SGObject.cpp.

## ◆ set_generic() [6/16]

 void set_generic ( )
inherited

Definition at line 98 of file SGObject.cpp.

## ◆ set_generic() [7/16]

 void set_generic ( )
inherited

Definition at line 103 of file SGObject.cpp.

## ◆ set_generic() [8/16]

 void set_generic ( )
inherited

Definition at line 108 of file SGObject.cpp.

## ◆ set_generic() [9/16]

 void set_generic ( )
inherited

Definition at line 113 of file SGObject.cpp.

## ◆ set_generic() [10/16]

 void set_generic ( )
inherited

Definition at line 118 of file SGObject.cpp.

## ◆ set_generic() [11/16]

 void set_generic ( )
inherited

Definition at line 123 of file SGObject.cpp.

## ◆ set_generic() [12/16]

 void set_generic ( )
inherited

Definition at line 128 of file SGObject.cpp.

## ◆ set_generic() [13/16]

 void set_generic ( )
inherited

Definition at line 133 of file SGObject.cpp.

## ◆ set_generic() [14/16]

 void set_generic ( )
inherited

Definition at line 138 of file SGObject.cpp.

## ◆ set_generic() [15/16]

 void set_generic ( )
inherited

Definition at line 143 of file SGObject.cpp.

## ◆ set_generic() [16/16]

 void set_generic ( )
inherited

set generic type to T

## ◆ set_global_io()

 void set_global_io ( SGIO * io )
inherited

set the io object

Parameters
 io io object to use

Definition at line 262 of file SGObject.cpp.

## ◆ set_global_parallel()

 void set_global_parallel ( Parallel * parallel )
inherited

set the parallel object

Parameters
 parallel parallel object to use

Definition at line 275 of file SGObject.cpp.

## ◆ set_global_version()

 void set_global_version ( Version * version )
inherited

set the version object

Parameters
 version version object to use

Definition at line 317 of file SGObject.cpp.

## ◆ set_kernel()

 void set_kernel ( CKernel * kernel )
virtualinherited

Method that sets the kernel that is used for performing the two-sample test. It is kept virtual so that sub-classes can perform other initialization tasks that has to be trigger every time a kernel is set/updated.

Parameters
 kernel The kernel instance.

Definition at line 66 of file TwoSampleTest.cpp.

## ◆ set_kernel_selection_strategy() [1/2]

 void set_kernel_selection_strategy ( EKernelSelectionMethod method, bool weighted = false )

Method that sets the specific kernel selection strategy based on the specific parameters provided. Please see class documentation for details. Use this method for every other strategy other than KSM_CROSS_VALIDATION.

Parameters
 method The kernel selection method as specified in EKernelSelectionMethod. weighted If true, then an weighted combination of the kernel is used after solving an optimization. If false, only a single kernel is selected among the provided ones.

Definition at line 89 of file MMD.cpp.

## ◆ set_kernel_selection_strategy() [2/2]

 void set_kernel_selection_strategy ( EKernelSelectionMethod method, index_t num_runs, index_t num_folds, float64_t alpha )

Method that sets the specific kernel selection strategy based on the specific parameters provided. Please see class documentation for details. Use this method for KSM_CROSS_VALIDATION.

Parameters
 method The kernel selection method as specified in EKernelSelectionMethod. num_runs The number of total runs of the cross-validation algorithm. num_folds The number of folds (k) to be used in k-fold stratified cross-validation. alpha The threshold to be used while performing test for the test-folds.

Definition at line 95 of file MMD.cpp.

## ◆ set_null_approximation_method()

 void set_null_approximation_method ( ENullApproximationMethod nmethod )

Method that sets the approach to be taken while approximating the null-samples.

The null-approximation method

Definition at line 149 of file MMD.cpp.

## ◆ set_num_null_samples()

 void set_num_null_samples ( index_t null_samples )

Method that sets the number of null-samples used for computing p-value.

Parameters
 null_samples Number of null-samples.

Definition at line 129 of file MMD.cpp.

## ◆ set_num_samples_p()

 void set_num_samples_p ( index_t num_samples_from_p )
inherited

Method that initializes the number of samples to be drawn from distribution $$\mathbf{P}$$. Please ensure to call this method if you are intending to use streaming data generators that generate the samples on the fly. For other types of features, the number of samples is set internally from the features object itself, therefore this method should not be used.

Parameters
 num_samples_from_p The CFeatures instance representing the samples from $$\mathbf{P}$$.

Definition at line 75 of file TwoDistributionTest.cpp.

## ◆ set_num_samples_q()

 void set_num_samples_q ( index_t num_samples_from_q )
inherited

Method that initializes the number of samples to be drawn from distribution $$\mathbf{Q}$$. Please ensure to call this method if you are intending to use streaming data generators that generate the samples on the fly. For other types of features, the number of samples is set internally from the features object itself, therefore this method should not be used.

Parameters
 num_samples_from_q The CFeatures instance representing the samples from $$\mathbf{Q}$$.

Definition at line 87 of file TwoDistributionTest.cpp.

## ◆ set_p()

 void set_p ( CFeatures * samples_from_p )
virtualinherited

Method that initializes the samples from $$\mathbf{P}$$. This method is kept virtual for the sub-classes to perform additional initialization tasks that have to be performed every time features are set/updated.

Parameters
 samples_from_p The CFeatures instance representing the samples from $$\mathbf{P}$$.

Definition at line 49 of file TwoDistributionTest.cpp.

## ◆ set_q()

 void set_q ( CFeatures * samples_from_q )
virtualinherited

Method that initializes the samples from $$\mathbf{Q}$$. This method is kept virtual for the sub-classes to perform additional initialization tasks that have to be performed every time features are set/updated.

Parameters
 samples_from_q The CFeatures instance representing the samples from $$\mathbf{Q}$$.

Definition at line 62 of file TwoDistributionTest.cpp.

## ◆ set_statistic_type()

 void set_statistic_type ( EStatisticType stype )

Method that sets the type of the estimator for MMD^2

Parameters
 stype The type of the estimator for MMD^2

Definition at line 139 of file MMD.cpp.

## ◆ set_train_test_mode()

 void set_train_test_mode ( bool on )
inherited

Method that enables/disables the training-testing mode. If this option is turned on, then the samples would be split in two pieces: one chunk would be used for training algorithms and the other chunk would be used for performing tests. If this option is turned off, the entire data would be used for performing the test. Before running any training algorithms, make sure to turn this mode on.

By default, the training-testing mode is turned off.

{set_train_test_ratio()}
Parameters
 on Whether to enable/disable the training-testing mode

Definition at line 66 of file HypothesisTest.cpp.

## ◆ set_train_test_ratio()

 void set_train_test_ratio ( float64_t ratio )
inherited

Method that specifies the ratio of training-testing data split for the algorithms. Note that this is NOT the percentage of samples to be used for training, rather the ratio of the number of samples to be used for training and that of testing.

By default, an equal 50-50 split (ratio = 1) is made.

{set_train_test_mode()}
Parameters
 ratio The ratio of the number of samples to be used for training and that of testing

Definition at line 71 of file HypothesisTest.cpp.

## ◆ shallow_copy()

 CSGObject * shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 226 of file SGObject.cpp.

## ◆ subscribe_to_parameters()

 void subscribe_to_parameters ( ParameterObserverInterface * obs )
inherited

Subscribe a parameter observer to watch over params

Definition at line 811 of file SGObject.cpp.

## ◆ unref()

 int32_t unref ( )
inherited

decrement reference counter and deallocate object if refcount is zero before or after decrementing it

Returns
reference count

Definition at line 200 of file SGObject.cpp.

## ◆ unset_generic()

 void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 337 of file SGObject.cpp.

## ◆ update_parameter_hash()

 void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 282 of file SGObject.cpp.

## ◆ io

 SGIO* io
inherited

io

Definition at line 600 of file SGObject.h.

inherited

parameters wrt which we can compute gradients

Definition at line 615 of file SGObject.h.

## ◆ m_hash

 uint32_t m_hash
inherited

Hash of parameter values

Definition at line 618 of file SGObject.h.

## ◆ m_model_selection_parameters

 Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 612 of file SGObject.h.

## ◆ m_parameters

 Parameter* m_parameters
inherited

parameters

Definition at line 609 of file SGObject.h.

## ◆ parallel

 Parallel* parallel
inherited

parallel

Definition at line 603 of file SGObject.h.

## ◆ version

 Version* version
inherited

version

Definition at line 606 of file SGObject.h.

The documentation for this class was generated from the following files:

SHOGUN Machine Learning Toolbox - Documentation